One major advantage of CT angiography in comparison to other imaging techniques such as magnetic resonance tomography (MR), PET (positron emission tomography), SPECT (single photon emission computed tomography) or 3D ultrasound is that, for example, the entire vascular system of the heart can be recorded in a single CT scan, by the use of contrast agent. The rapid further development of multilayer CT systems indicates that it will very shortly be possible to also image a plurality of phases of the heartbeat in the form of 3D data records in a short time sequence. The special diagnostic value of this technique is, in particular, that it is now possible to trace the entire heart anatomy from the diastolic state to the systolic state of the heart.
The examination of the coronary vessels (coronaries) is of particular interest in this case, since these can cause an inadequate supply to the myocardium, for example when plaque deposits result in a stenosis. This very frequently leads to a coronary infarct, and quite often to the death of the patient as well. For quantitative evaluation, in particular measurement of stenoses or plaque deposits, the appropriate areas of the vessel structure must be segmented from the 3D image data. Algorithm segmentation of the anatomical vessel structures, that is to say separation of the gray-scale values which represent the vessels in the 3D volume data record from the other anatomical structures, is therefore of major importance for the cardiological/radiological examination of the coronary vessels.
Various methods for segmentation of coronary vessels from 3D image data records are already known, such as techniques which are known by the expressions region growing technique, threshold value methods or level set methods. One example of a segmentation technique such as this for segmentation of vessel structures can be found in the publication by T. Boskamp et al., “New Vessel Analysis Tool for Morphometric Quantification and Visualisation of Vessels in CT and MR Imaging Data Sets” Radiographics 2004, 24, 287-297. However, when using algorithmic segmentation, surrounding tissue which does not belong to the coronary vessels is also frequently erroneously included in the segmentation process.
Based on the knowledge of the inventors, each individual 3D image data record is generally segmented independently of the others during the segmentation of vessel structures from a sequence of 3D image data records recorded in a time sequence, also referred to in the following text as a 4D image data record. This is a time-consuming and computation-intensive process for the segmentation of the 3D image data records for a complete heart cycle. However, tracing of the coronary vessel system in the time domain and thus segmentation of the individual 3D image data records from a sequence are actually highly important for functional evaluations of the heart.
U.S. Pat. No. 6,169,817 B1 describes a method for segmentation of anatomical structures from a sequence of 3D image data records recorded in a time sequence, which method is based on the idea of using segmentation results of a first 3D image data record for segmentation of the further 3D image data records in order to save computation time. In the method in this document, anatomical structures are first of all segmented from the first 3D image data record.
For the next step, it is necessary to know the elastic constants of the various segmented structures. A network of selected points is then formed, with which the appropriate constants are associated. Movement of each individual point to the next image is estimated on the basis of this network of points and the known material constants.
This recalculated network of points is then compared with this next image in order to produce a point-to-point correspondence by minimizing the total energy. In this way, the estimated movement of the individual pixels is matched to the next image, so that the segmentation can then be transferred from the first image. However, the method is highly complex owing to the material data and equations of motion that are required.